Soft assignment of visual words as Linear Coordinate Coding and optimisation of its reconstruction error
Soft assignment of visual words as Linear Coordinate Coding and optimisation of its reconstruction error
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DOI：
10.1109/icip.2011.6116129
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发表时间：
2011-12
期刊：
影响因子：
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通讯作者：
Piotr Koniusz;K. Mikolajczyk
中科院分区：
文献类型：
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作者：
Piotr Koniusz;K. Mikolajczyk
视觉词汇不确定性也被称为软分配是一种成熟的技术，用于通过将图像描述符灵活地分配给视觉词汇表来将图像表示为直方图。最近，处理对象类别识别的社区的注意力已经被吸引到线性坐标编码方法。在这项工作中，我们专注于软分配，因为它在竞争激烈的方法中产生了良好的效果。我们表明，人们可以采取两种观点软分配：高斯混合模型或特殊情况下的线性坐标编码的方法。后一种观点有助于我们提出如何优化软分配的平滑因子，以最大限度地减少描述符重建误差和最大限度地提高分类性能。反过来，这使得建立这个参数的繁琐的交叉验证变得不必要，并使其成为一种方便的技术。我们展示了两个图像数据集和几种类型的描述符的这种优化分配的最先进的性能。
Visual Word Uncertainty also referred to as Soft Assignment is a well established technique for representing images as histograms by flexible assignment of image descriptors to a visual vocabulary. Recently, an attention of the community dealing with the object category recognition has been drawn to Linear Coordinate Coding methods. In this work, we focus on Soft Assignment as it yields good results amidst competitive methods. We show that one can take two views on Soft Assignment: an approach derived from Gaussian Mixture Model or special case of Linear Coordinate Coding. The latter view helps us propose how to optimise smoothing factor of Soft Assignment in a way that minimises descriptor reconstruction error and maximises classification performance. In turns, this renders tedious cross-validation towards establishing this parameter unnecessary and yields it a handy technique. We demonstrate state-of-the-art performance of such optimised assignment on two image datasets and several types of descriptors.